Non-transitory computer-readable recording medium, estimation method, learned model, and method of generating learned model

Abstract

A non-transitory computer-readable recording medium having stored therein a program causes a computer to execute a process. The process includes acquiring first information related to a linear circuit having a plurality of ports for receiving or outputting a high frequency signal, and second information related to a frequency of the high frequency signal, estimating, when the second information indicates that the frequency is 0 Hz, an S-parameter for two ports of the plurality of ports when the frequency is 0 Hz, from the first information based on a first learned model, and estimating, when the second information indicates that the frequency is other than 0 Hz, an S-parameter at the frequency from the first information and the second information, based on a second learned model. The first and the second learned models are generated by performing machine learning on plural pieces of first training data and second training data, respectively.

The invention claimed is: 
     
         1 . A non-transitory computer-readable recording medium having stored therein a program for causing a computer to execute a process, the process comprising:
 acquiring first information related to a linear circuit having a plurality of ports for receiving or outputting a high frequency signal, and second information related to a frequency of the high frequency signal;   estimating, when the second information indicates that the frequency is 0 Hz, an S-parameter for two ports of the plurality of ports when the frequency is 0 Hz, from the first information based on a first learned model; and   estimating, when the second information indicates that the frequency is other than 0 Hz, an S-parameter at the frequency from the first information and the second information, based on a second learned model, wherein   the first learned model is generated by performing machine learning on a plurality of pieces of first training data, the plurality of pieces of first training data defining a relationship between a plurality of pieces of first information of the linear circuit and a plurality of S-parameters, each of the plurality of S-parameters being calculated for a corresponding one of the plurality of pieces of first information when the frequency is 0 Hz, and   the second learned model is generated by performing machine learning on a plurality of pieces of second training data, the plurality of pieces of second training data defining a relationship among the plurality of pieces of first information of the linear circuit, a plurality of frequencies, and a plurality of S-parameters, each of the plurality of frequencies including a frequency other than 0 Hz, each of the plurality of S-parameters being calculated for a corresponding one of the plurality of pieces of first information and a corresponding one of the plurality of frequencies.   
     
     
         2 . The non-transitory computer-readable recording medium according to  claim 1 , wherein
 each of the plurality of S-parameters in the plurality of pieces of first training data is normalized by a first maximum value and a first minimum value, and   each of the plurality of S-parameters in the plurality of pieces of second training data is normalized by a second maximum value and a second minimum value.   
     
     
         3 . The non-transitory computer-readable recording medium according to  claim 2 , wherein a difference between the first maximum value and the first minimum value is smaller than a difference between the second maximum value and the second minimum value. 
     
     
         4 . The non-transitory computer-readable recording medium according to  claim 3 , wherein
 the estimating the S-parameter based on the first learned model estimates the S-parameter by decoding a value generated based on the first learned model, based on the first maximum value and the first minimum value, and   the estimating the S-parameter based on the second learned model estimates the S-parameter by decoding a value generated based on the second learned model, based on the second maximum value and the second minimum value.   
     
     
         5 . The non-transitory computer-readable recording medium according to  claim 1 , wherein the two ports are opened or short-circuited when the frequency is 0 Hz. 
     
     
         6 . The non-transitory computer-readable recording medium according to  claim 5 , wherein when the two ports are port  1  and port  2 , the S-parameter includes S 21 . 
     
     
         7 . The non-transitory computer-readable recording medium according to  claim 1 , wherein
 the second learned model is generated by performing machine learning on the plurality of pieces of second training data, the plurality of pieces of second training data defining a relationship among the plurality of pieces of first information of the linear circuit, a plurality of frequencies including a frequency of 0 Hz and a frequency other than 0 Hz, and the plurality of S-parameters, each of the plurality of S-parameters being calculated for a corresponding one of the plurality of pieces of first information and a corresponding one of the plurality of frequencies.   
     
     
         8 . An estimation method comprising:
 acquiring first information related to a linear circuit having a plurality of ports for receiving or outputting a high frequency signal, and second information related to a frequency of the high frequency signal;   estimating, when the second information indicates that the frequency is 0 Hz, an S-parameter for two ports of the plurality of ports when the frequency is 0 Hz, from the first information based on a first learned model; and   estimating, when the second information indicates that the frequency is other than 0 Hz, an S-parameter at the frequency from the first information and the second information, based on a second learned model, wherein   the first learned model is generated by performing machine learning on a plurality of pieces of first training data, the plurality of pieces of first training data defining a relationship between a plurality of pieces of first information of the linear circuit and a plurality of S-parameters, each of the plurality of S-parameters being calculated for a corresponding one of the plurality of pieces of first information when the frequency is 0 Hz, and   the second learned model is generated by performing machine learning on a plurality of pieces of second training data, the plurality of pieces of second training data defining a relationship among the plurality of pieces of first information of the linear circuit, a plurality of frequencies, and a plurality of S-parameters, each of the plurality of frequencies including a frequency other than 0 Hz, each of the plurality of S-parameters being calculated for a corresponding one of the plurality of pieces of first information and a corresponding one of the plurality of frequencies.   
     
     
         9 . A learned model for estimating an S-parameter for two ports of a plurality of ports for receiving or outputting a high frequency signal, based on first information related to a linear circuit having the plurality of ports and second information related to a frequency of the high frequency signal, the learned model comprising:
 a first learned model generated by performing machine learning on a plurality of pieces of first training data, the plurality of pieces of first training data defining a relationship between a plurality of pieces of first information and a plurality of S-parameters, each of the plurality of S-parameters being calculated for a corresponding one of the plurality of pieces of first information when the frequency is 0 Hz; and   a second learned model generated by performing machine learning on a plurality of pieces of second training data, the plurality of pieces of second training data defining a relationship among the plurality of pieces of first information, a plurality of frequencies, and a plurality of S-parameters, each of the plurality of frequencies including a frequency other than 0 Hz, each of the plurality of S-parameters being calculated for a corresponding one of the plurality of pieces of first information and a corresponding one of the plurality of frequencies.   
     
     
         10 . A method of generating a learned model for estimating an S-parameter for two ports of a plurality of ports for receiving or outputting a high frequency signal, based on first information related to a linear circuit having the plurality of ports and second information related to a frequency of the high frequency signal, the method comprising:
 generating a first learned model by performing machine learning on a plurality of pieces of first training data, the plurality of pieces of first training data defining a relationship between a plurality of pieces of first information and a plurality of S-parameters, each of the plurality of S-parameters being calculated for a corresponding one of the plurality of pieces of first information when the frequency is 0 Hz; and   generating a second learned model by performing machine learning on a plurality of pieces of second training data, the plurality of pieces of second training data defining a relationship among the plurality of pieces of first information, a plurality of frequencies, and a plurality of S-parameters, each of the plurality of frequencies including a frequency other than 0 Hz, each of the plurality of S-parameters being calculated for a corresponding one of the plurality of pieces of first information and a corresponding one of the plurality of frequencies.